Preparing for the Clinical Stage : Lab Development and Testing of Socially Assistive Robot for Physical Health Assessments
Bibliographic record
Abstract
The integration of socially assistive robots (SARs) in healthcare has the potential to revolutionize physical health assessments by providing standardized, reliable, and engaging methods for evaluating functional mobility and muscle strength. This paper presents the development and testing of a SAR system designed specifically for standardized physical assessments in patients with rare diseases with mobility-related impairements such as ataxias and muscular dystrophies. Utilizing the TEMI robot, our study focused on three standardized tests: the 30-Second Chair Stand Test, the 10-Meter Walk Test, and the Grip Strength Test. Preliminary results from lab experiments with healthy subjects indicate strong correlations between manual and robotic measurements, particularly for knee angles and walk times, demonstrating the system’s accuracy and consistency. However, challenges were still noted, like the need for more interactive procedures and clearer instructions. These findings highlight both the potential and the hurdles in deploying SARs for physical assessments. This research contributes to the broader field of health informatics and robotic-assisted interventions, offering insights into the design, development, and testing of SAR systems for clinical use, setting the stage for their eventual integration into clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".